The study aims to explore the roles of Massive Open Online Courses (MOOCs) based on deep learning in college students’ English grammar teaching. The data are collected using a survey. After the experimental data are analyzed, it is found that students have a low sense of happiness and satisfaction and are unwilling to practice oral English and learn language points in English learning. They think that college English learning only meets the needs of CET-4 and CET-6 and does not take it as the ultimate learning goal. After the necessity and problems in English grammar teaching are discussed, the advantages of flipped classrooms of MOOCs are discussed in English grammar teaching. A teaching platform is constructed to study the foreign language teaching mode under MOOCs, and classroom teaching is combined with the advantages of MOOCs following the principle of “teaching students according to their personalities” to improve the listening, speaking, reading, writing, and translation skills of foreign language majors. The results show that high-quality online teaching resources and the deep learning-based teaching environment can provide a variety of interactive tools, by which students can communicate with their peers and teachers online. Sharing open online communication, classroom discussion, and situational simulation can enhance teachers’ deep learning ability, like the ability to communication and transfer thoughts. Constructivism with interaction as the core can help students grasp new knowledge easily. Extensive communication and interaction are important ways for learning and thinking. The new model provides students with profound learning experience, expands the teaching resources of MOOCs around the world, and maximizes the interaction between online and offline teachers and students, making knowledge widely rooted in the campus and realizing the combination of online resources and campus classroom teaching. Students can learn the knowledge through autonomous learning and discussion before class, which greatly broadens the learning time and space. In the classroom and after class, the internalization and sublimation of knowledge are completed through group cooperation, inquiry learning, scenario simulation, display, and evaluation, promoting students to know about new knowledge and highlighting the dominant position of students.

This study uses an end-to-end encoder-decoder structure to build a machine translation model, allowing the machine to automatically learn features and transform the corpus data into distributed representations. The word vector uses a neural network to achieve direct mapping. Research on constructing neural machine translation models for different neural network structures. Based on the translation model of the LSTM network, the gate valve mechanism reduces the gradient attenuation and improves the ability to process long-distance sequences. Based on the GRU network structure, the simplified processing is performed on the basis of it, which reduces the training complexity and achieves good performance. Aiming at the problem that some source language sequences of any length in the encoder are encoded into fixed-dimensional background vectors, the attention mechanism is introduced to dynamically adjust the degree of influence of the source language context on the target language sequence, and the translation model’s ability to deal with long-distance dependencies is improved. In order to better reflect the context information, this study further proposes a machine translation model based on two-way GRU and compares and analyzes multiple translation models to verify the effectiveness of the model’s performance improvement.

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